Do I want a mixed model for fractional factorial designs? I have created a D-efficient fractional factorial design of 48 combinations from a total of 192 possible combinations (4x2x2x3x2x2).
For the experiment, I plan to have 4 runs in 12 blocks and 40 individuals for each block completing the 4 runs.
Each individual sees 4 text vignettes and gives a 10 point rating for each. In each vignette, the 6 dimensions vary.
So I would have 480 individuals participating and 1920 observations in total.
To my knowledge, this repeated measurement of individuals (4 times) needs to be accounted for.
Is a mixed model an appropriate way to do so? I basically would have data from runs nested in individuals.
I have heard some arguments, that a mixed model is not appropriate for this design, since there is no theoretically interesting variance at the lower level, since the treatment combinations are essentially randomized?
The main research interest is the marginal effect of the 6 dimensions.
But to be honest, I am still very confused about this. I know that it is possible to estimate a mixed model with this data, but I am unsure if its the right thing to do given the design.
 A: 
I have heard some arguments, that a mixed model is not appropriate for this design, since there is no theoretically interesting variance at the lower level, since the treatment combinations are essentially randomized?

I would be interested to know where you read those arguments. 

To my knowledge, this repeated measurement of individuals (4 times) needs to be accounted for. Is a mixed model an appropriate way to do so?

I don't see any reason why you shouldn't use a mixed effects model. You have repeated measurements, and fitting random intercepts for each level of clustering is a good way to account for the non-independence of observations within each cluster.
It is not completely clear from the question what the hierarchy of nested factors are, but it sounds a lot like individuals are nested within blocks. That is each subject is measured 4 times (4 runs per subject) and each subject belongs to one of 12 blocks. Thus your model could look something like:
outcome ~ treatments + (1 | block/subject)

If the allocation to blocks is conmpletely random and the randomization was successful then you may not need block in the random structure.
